Urban tinkering

Cities are currently experiencing serious, multifaceted impacts from global environmental change, especially climate change, and the degree to which they will need to cope with and adapt to such challenges will continue to increase. A complex systems approach inspired by evolutionary theory can inform strategies for policies and interventions to deal with growing urban vulnerabilities. Such an approach would guide the design of new (and redesign of existing) urban structures, while promoting innovative integration of grey, green and blue infrastructure in service of environmental and health objectives. Moreover, it would contribute to more flexible, effective policies for urban management and the use of urban space. Four decades ago, in a seminal paper in Science, the French evolutionary biologist and philosopher Francois Jacob noted that evolution differs significantly in its characteristic modes of action from processes that are designed and engineered de novo (Jacob in Science 196(4295):1161–1166, 1977). He labeled the evolutionary process “tinkering”, recognizing its foundation in the modification and molding of existing traits and forms, with occasional dramatic shifts in function in the context of changing conditions. This contrasts greatly with conventional engineering and design approaches that apply tailor-made materials and tools to achieve well-defined functions that are specified a priori. We here propose that urban tinkering is the application of evolutionary thinking to urban design, engineering, ecological restoration, management and governance. We define urban tinkering as:

A mode of operation, encompassing policy, planning and management processes, that seeks to transform the use of existing and design of new urban systems in ways that diversify their functions, anticipate new uses and enhance adaptability, to better meet the social, economic and ecological needs of cities under conditions of deep uncertainty about the future.

This approach has the potential to substantially complement and augment conventional urban development, replacing predictability, linearity and monofunctional design with anticipation of uncertainty and non-linearity and design for multiple, potentially shifting functions. Urban tinkering can function by promoting a diversity of small-scale urban experiments that, in aggregate, lead to large-scale often playful innovative solutions to the problems of sustainable development. Moreover, the tinkering approach is naturally suited to exploring multi-functional uses and approaches (e.g., bricolage) for new and existing urban structures and policies through collaborative engagement and analysis. It is thus well worth exploring as a means of delivering co-benefits for environment and human health and wellbeing. Indeed, urban tinkering has close ties to systems approaches, which often are recognized as critical to sustainable development. We believe this concept can help forge much-closer, much-needed ties among engineers, architects, evolutionary ecologists, health specialists, and numerous other urban stakeholders in developing innovative, widely beneficial solutions for society and contribute to successful implementation of SDG11 and the New Urban Agenda.

     

Temporal and spatial changes of PAH concentrations in Mytilus galloprovincialis from Ria de Vigo (NW Spain)

The aim of this study was to establish the temporal trends and spatial distribution of Polycyclic Aromatic Hydrocarbons (PAHs) in Ria de Vigo by means of studying the PAH burden in wild mussels. The samples were collected in eight sites along the margins of the Ria de Vigo during November from 1998 to 2008 and Polycyclic Aromatic Hydrocarbon contents were determined using high performance liquid chromatography–fluorescence derivatization (HPLC–FLD). Quality of chemical analysis was guaranteed by participation in QUASIMEME intercalibration exercises carried out during the period of analysis. The concentrations for the sum of 13 PAHs were in the range 24–480 μg/kg dw, typical of an urban and industrialized area (50–500 μg/kg dw) except for a punctual input in La Guía in 1998 that led to a concentration above 1,000 μg/kg dw. In general, the sites in the outermost section of the estuary (ría) showed the lowest values, with an increasing gradient in the medium and inner parts of the ría. Temporal patterns show, in general, a downward trend. The relative proportion of 3 and 4 ring PAHs also varies with the site, the former being predominant in the outermost area and the latter in the medium and inner parts of the ría. This is consistently related with the sources and processes taking place in different areas of the estuary.     

Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications - including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for some variables. Model performance varied greatly according to the level of regularization. Intermediate regularization consistently led to the best models, with decreased performance at low and generally at high regularization. Optimal levels of regularization differed between sample-size-dependent and sample-size-independent approaches, but both reached similar levels of maximal performance. In several cases, the optimal regularization value was different from (usually higher than) the default one. Models calibrated with both linear and quadratic features outperformed those made with just linear features. Results were remarkably consistent across the examined sample sizes. Models made with few and biased localities achieved high predictive ability when appropriate regularization was employed and optimal model complexity was identified. Species-specific tuning of model settings can have great benefits over the use of default settings.     

Immunoassay-based screening of polychlorinated biphenyls (PCB) in sediments: requirements for a new generation of test kits

Polychlorinated biphenyls (PCBs) have been proposed for the inclusion in the European Water Framework Directive (WFD) priority list, currently under revision. Various screening methods have been employed for PCB determination in different environmental matrixes in the last decades, immunoassays being one of the most employed. A literature review reveals that the enzyme-linked immunosorbent assay (ELISA) is the most commonly applied immunoassay for PCB determination in the environment. However, its application to sediments is very limited. A suitability assessment of immunoassay-based analysis for PCB screening in sediments is presented in this work. The significance of available immunoassay-based test kits under the current environmental pollution scenario and their performance against the sensitivity and specificity requirements dictated by the WFD for PCB analysis in sediments is discussed. For example, current detection limits of available test kits for PCB determination in sediments may not be enough for compliance checking under the WFD. In addition, concentration expressed as Aroclor equivalents does not seem to be the way forward. A proposal for adapting available test kits in order to become more suitable tools for PCB screening in sediments is also presented in this study.     

Using selection functions to describe changes in environmental variables

The selection function (which shows how the frequency of sampling units with the value X = x at one point in time must change in order to produce the distribution that occurs at a later point in time) is proposed for describing the changes over time in an environmentally important variable X. It is shown that the theory of selection functions as used in the study of natural selection and resource selection by animals requires some modifications in this new application and that a selection function is a useful tool in long-term monitoring studies because all changes in a distribution can be examined (rather than just changes in single parameters such as the mean), and because graphical presentations of the selection function are easy for non-statisticians to understand. Estimation of the selection function is discussed using a method appropriate for normal distributions and bootstrapping is suggested as a method for assessing the precision of estimates and for testing for significant differences between samples taken at different times. Methods are illustrated using data on water chemical variables from a study of the effects of acid precipitation in Norway.     

Climate Change Adaptation for the US National Wildlife Refuge System

Since its establishment in 1903, the National Wildlife Refuge System (NWRS) has grown to 635 units and 37 Wetland Management Districts in the United States and its territories. These units provide the seasonal habitats necessary for migratory waterfowl and other species to complete their annual life cycles. Habitat conversion and fragmentation, invasive species, pollution, and competition for water have stressed refuges for decades, but the interaction of climate change with these stressors presents the most recent, pervasive, and complex conservation challenge to the NWRS. Geographic isolation and small unit size compound the challenges of climate change, but a combined emphasis on species that refuges were established to conserve and on maintaining biological integrity, diversity, and environmental health provides the NWRS with substantial latitude to respond. Individual symptoms of climate change can be addressed at the refuge level, but the strategic response requires system-wide planning. A dynamic vision of the NWRS in a changing climate, an explicit national strategic plan to implement that vision, and an assessment of representation, redundancy, size, and total number of units in relation to conservation targets are the first steps toward adaptation. This adaptation must begin immediately and be built on more closely integrated research and management. Rigorous projections of possible futures are required to facilitate adaptation to change. Furthermore, the effective conservation footprint of the NWRS must be increased through land acquisition, creative partnerships, and educational programs in order for the NWRS to meet its legal mandate to maintain the biological integrity, diversity, and environmental health of the system and the species and ecosystems that it supports.     

Factors Related to Spatial Patterns of Rural Land Fragmentation in Texas

Fragmentation of family-owned farms and ranches has been identified as the greatest single threat to wildlife habitat, water supply, and the long-term viability of agriculture in Texas. However, an integrative framework for insights into the pathways of land use change has been lacking. The specific objectives of the study are to test the hypotheses that the nonagricultural value (NAV) of rural land is a reliable indicator of trends in land fragmentation and that NAV in Texas is spatially correlated with population density, and to explore the idea that recent changes in property size patterns are better represented by a categorical model than by one that reflects incremental changes. We propose that the State-and-Transition model, developed to describe the dynamics of semi-arid ecosystems, provides an appropriate conceptual framework for characterizing categorical shifts in rural property patterns. Results suggest that changes in population density are spatially correlated with NAV and farm size, and that rural property size is spatially correlated with changes in NAV. With increasing NAV, the proportion of large properties tends to decrease while the area represented by small properties tends to increase. Although a correlation exists between NAV and population density, it is the trend in NAV that appears to be a stronger predictor of land fragmentation. The empirical relationships established herein, viewed within the conceptual framework of the State-and-Transition model, can provide a useful tool for evaluating land use policies for maintaining critical ecosystem services delivered from privately owned land in private land states, such as Texas.